{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2 DataLoaderの作成\n",
    "\n",
    "- 本ファイルでは、PSPNetなどセマンティックセグメンテーション用のDatasetとDataLoaderを作成します。VOC2012データセットを対象とします。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 学習目標\n",
    "\n",
    "1.\tセマンティックセグメンテーションで使用するDatasetクラス、DataLoaderクラスを作成できるようになる\n",
    "2.\tPSPNetの前処理およびデータオーギュメンテーションの処理内容を理解する"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 事前準備\n",
    "\n",
    "- 書籍の指示に従い、本章で使用するデータをダウンロードします。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# パッケージのimport\n",
    "import os.path as osp\n",
    "from PIL import Image\n",
    "\n",
    "import torch.utils.data as data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 画像データ、アノテーションデータへのファイルパスのリストを作成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def make_datapath_list(rootpath):\n",
    "    \"\"\"\n",
    "    学習、検証の画像データとアノテーションデータへのファイルパスリストを作成する。\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    rootpath : str\n",
    "        データフォルダへのパス\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    ret : train_img_list, train_anno_list, val_img_list, val_anno_list\n",
    "        データへのパスを格納したリスト\n",
    "    \"\"\"\n",
    "\n",
    "    # 画像ファイルとアノテーションファイルへのパスのテンプレートを作成\n",
    "    imgpath_template = osp.join(rootpath, 'JPEGImages', '%s.jpg')\n",
    "    annopath_template = osp.join(rootpath, 'SegmentationClass', '%s.png')\n",
    "\n",
    "    # 訓練と検証、それぞれのファイルのID（ファイル名）を取得する\n",
    "    train_id_names = osp.join(rootpath + 'ImageSets/Segmentation/train.txt')\n",
    "    val_id_names = osp.join(rootpath + 'ImageSets/Segmentation/val.txt')\n",
    "\n",
    "    # 訓練データの画像ファイルとアノテーションファイルへのパスリストを作成\n",
    "    train_img_list = list()\n",
    "    train_anno_list = list()\n",
    "\n",
    "    for line in open(train_id_names):\n",
    "        file_id = line.strip()  # 空白スペースと改行を除去\n",
    "        img_path = (imgpath_template % file_id)  # 画像のパス\n",
    "        anno_path = (annopath_template % file_id)  # アノテーションのパス\n",
    "        train_img_list.append(img_path)\n",
    "        train_anno_list.append(anno_path)\n",
    "\n",
    "    # 検証データの画像ファイルとアノテーションファイルへのパスリストを作成\n",
    "    val_img_list = list()\n",
    "    val_anno_list = list()\n",
    "\n",
    "    for line in open(val_id_names):\n",
    "        file_id = line.strip()  # 空白スペースと改行を除去\n",
    "        img_path = (imgpath_template % file_id)  # 画像のパス\n",
    "        anno_path = (annopath_template % file_id)  # アノテーションのパス\n",
    "        val_img_list.append(img_path)\n",
    "        val_anno_list.append(anno_path)\n",
    "\n",
    "    return train_img_list, train_anno_list, val_img_list, val_anno_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/VOCdevkit/VOC2012/JPEGImages/2007_000032.jpg\n",
      "./data/VOCdevkit/VOC2012/SegmentationClass/2007_000032.png\n"
     ]
    }
   ],
   "source": [
    "# 動作確認 ファイルパスのリストを取得\n",
    "rootpath = \"./data/VOCdevkit/VOC2012/\"\n",
    "\n",
    "train_img_list, train_anno_list, val_img_list, val_anno_list = make_datapath_list(\n",
    "    rootpath=rootpath)\n",
    "\n",
    "print(train_img_list[0])\n",
    "print(train_anno_list[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Datasetの作成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# データ処理のクラスとデータオーギュメンテーションのクラスをimportする\n",
    "from utils.data_augumentation import Compose, Scale, RandomRotation, RandomMirror, Resize, Normalize_Tensor\n",
    "\n",
    "\n",
    "class DataTransform():\n",
    "    \"\"\"\n",
    "    画像とアノテーションの前処理クラス。訓練時と検証時で異なる動作をする。\n",
    "    画像のサイズをinput_size x input_sizeにする。\n",
    "    訓練時はデータオーギュメンテーションする。\n",
    "\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    input_size : int\n",
    "        リサイズ先の画像の大きさ。\n",
    "    color_mean : (R, G, B)\n",
    "        各色チャネルの平均値。\n",
    "    color_std : (R, G, B)\n",
    "        各色チャネルの標準偏差。\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, input_size, color_mean, color_std):\n",
    "        self.data_transform = {\n",
    "            'train': Compose([\n",
    "                Scale(scale=[0.5, 1.5]),  # 画像の拡大\n",
    "                RandomRotation(angle=[-10, 10]),  # 回転\n",
    "                RandomMirror(),  # ランダムミラー\n",
    "                Resize(input_size),  # リサイズ(input_size)\n",
    "                Normalize_Tensor(color_mean, color_std)  # 色情報の標準化とテンソル化\n",
    "            ]),\n",
    "            'val': Compose([\n",
    "                Resize(input_size),  # リサイズ(input_size)\n",
    "                Normalize_Tensor(color_mean, color_std)  # 色情報の標準化とテンソル化\n",
    "            ])\n",
    "        }\n",
    "\n",
    "    def __call__(self, phase, img, anno_class_img):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        ----------\n",
    "        phase : 'train' or 'val'\n",
    "            前処理のモードを指定。\n",
    "        \"\"\"\n",
    "        return self.data_transform[phase](img, anno_class_img)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class VOCDataset(data.Dataset):\n",
    "    \"\"\"\n",
    "    VOC2012のDatasetを作成するクラス。PyTorchのDatasetクラスを継承。\n",
    "\n",
    "    Attributes\n",
    "    ----------\n",
    "    img_list : リスト\n",
    "        画像のパスを格納したリスト\n",
    "    anno_list : リスト\n",
    "        アノテーションへのパスを格納したリスト\n",
    "    phase : 'train' or 'test'\n",
    "        学習か訓練かを設定する。\n",
    "    transform : object\n",
    "        前処理クラスのインスタンス\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, img_list, anno_list, phase, transform):\n",
    "        self.img_list = img_list\n",
    "        self.anno_list = anno_list\n",
    "        self.phase = phase\n",
    "        self.transform = transform\n",
    "\n",
    "    def __len__(self):\n",
    "        '''画像の枚数を返す'''\n",
    "        return len(self.img_list)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        '''\n",
    "        前処理をした画像のTensor形式のデータとアノテーションを取得\n",
    "        '''\n",
    "        img, anno_class_img = self.pull_item(index)\n",
    "        return img, anno_class_img\n",
    "\n",
    "    def pull_item(self, index):\n",
    "        '''画像のTensor形式のデータ、アノテーションを取得する'''\n",
    "\n",
    "        # 1. 画像読み込み\n",
    "        image_file_path = self.img_list[index]\n",
    "        img = Image.open(image_file_path)   # [高さ][幅][色RGB]\n",
    "\n",
    "        # 2. アノテーション画像読み込み\n",
    "        anno_file_path = self.anno_list[index]\n",
    "        anno_class_img = Image.open(anno_file_path)   # [高さ][幅]\n",
    "\n",
    "        # 3. 前処理を実施\n",
    "        img, anno_class_img = self.transform(self.phase, img, anno_class_img)\n",
    "\n",
    "        return img, anno_class_img\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 475, 475])\n",
      "torch.Size([475, 475])\n",
      "(tensor([[[ 1.6667,  1.5125,  1.5639,  ...,  1.7523,  1.6667,  1.7009],\n",
      "         [ 1.5810,  1.4269,  1.4783,  ...,  1.7009,  1.6153,  1.6495],\n",
      "         [ 1.5639,  1.4098,  1.4440,  ...,  1.6838,  1.5982,  1.6324],\n",
      "         ...,\n",
      "         [-0.4739, -0.4911, -0.5424,  ...,  1.2557,  1.1872,  1.2214],\n",
      "         [-0.5596, -0.4911, -0.4911,  ...,  1.2385,  1.1872,  1.2214],\n",
      "         [-0.6281, -0.3883, -0.3369,  ...,  1.2385,  1.1872,  1.2214]],\n",
      "\n",
      "        [[ 1.8333,  1.6758,  1.7283,  ...,  1.9209,  1.8333,  1.8683],\n",
      "         [ 1.7458,  1.5882,  1.6408,  ...,  1.8683,  1.7808,  1.8158],\n",
      "         [ 1.7283,  1.5707,  1.6057,  ...,  1.8508,  1.7633,  1.7983],\n",
      "         ...,\n",
      "         [-0.5826, -0.6001, -0.6527,  ...,  1.4132,  1.3431,  1.3431],\n",
      "         [-0.6702, -0.6001, -0.6001,  ...,  1.3957,  1.3431,  1.3431],\n",
      "         [-0.7402, -0.4951, -0.4426,  ...,  1.3957,  1.3431,  1.3431]],\n",
      "\n",
      "        [[ 2.0474,  1.8905,  1.9428,  ...,  2.1346,  2.0474,  2.0823],\n",
      "         [ 1.9603,  1.8034,  1.8557,  ...,  2.0823,  1.9951,  2.0300],\n",
      "         [ 1.9428,  1.7860,  1.8208,  ...,  2.0648,  1.9777,  2.0125],\n",
      "         ...,\n",
      "         [-0.6367, -0.6541, -0.7064,  ...,  1.6291,  1.5594,  1.5768],\n",
      "         [-0.7238, -0.6541, -0.6541,  ...,  1.6117,  1.5594,  1.5768],\n",
      "         [-0.7936, -0.5495, -0.4973,  ...,  1.6117,  1.5594,  1.5768]]]), tensor([[0, 0, 0,  ..., 0, 0, 0],\n",
      "        [0, 0, 0,  ..., 0, 0, 0],\n",
      "        [0, 0, 0,  ..., 0, 0, 0],\n",
      "        ...,\n",
      "        [0, 0, 0,  ..., 0, 0, 0],\n",
      "        [0, 0, 0,  ..., 0, 0, 0],\n",
      "        [0, 0, 0,  ..., 0, 0, 0]], dtype=torch.uint8))\n"
     ]
    }
   ],
   "source": [
    "# 動作確認\n",
    "\n",
    "# (RGB)の色の平均値と標準偏差\n",
    "color_mean = (0.485, 0.456, 0.406)\n",
    "color_std = (0.229, 0.224, 0.225)\n",
    "\n",
    "# データセット作成\n",
    "train_dataset = VOCDataset(train_img_list, train_anno_list, phase=\"train\", transform=DataTransform(\n",
    "    input_size=475, color_mean=color_mean, color_std=color_std))\n",
    "\n",
    "val_dataset = VOCDataset(val_img_list, val_anno_list, phase=\"val\", transform=DataTransform(\n",
    "    input_size=475, color_mean=color_mean, color_std=color_std))\n",
    "\n",
    "# データの取り出し例\n",
    "print(val_dataset.__getitem__(0)[0].shape)\n",
    "print(val_dataset.__getitem__(0)[1].shape)\n",
    "print(val_dataset.__getitem__(0))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DataLoaderを作成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([8, 3, 475, 475])\n",
      "torch.Size([8, 475, 475])\n"
     ]
    }
   ],
   "source": [
    "# データローダーの作成\n",
    "\n",
    "batch_size = 8\n",
    "\n",
    "train_dataloader = data.DataLoader(\n",
    "    train_dataset, batch_size=batch_size, shuffle=True)\n",
    "\n",
    "val_dataloader = data.DataLoader(\n",
    "    val_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "# 辞書オブジェクトにまとめる\n",
    "dataloaders_dict = {\"train\": train_dataloader, \"val\": val_dataloader}\n",
    "\n",
    "# 動作の確認\n",
    "batch_iterator = iter(dataloaders_dict[\"val\"])  # イタレータに変換\n",
    "imges, anno_class_imges = next(batch_iterator)  # 1番目の要素を取り出す\n",
    "print(imges.size())  # torch.Size([8, 3, 475, 475])\n",
    "print(anno_class_imges.size())  # torch.Size([8, 3, 475, 475])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# （付録）Datasetから画像を取り出し、描画する"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 訓練画像の描画"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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aImIDvUD4ZmZ+vzS3vu4FmfkK8CS9YyKbI2LhdPz+2t6qu2y/FHh5upXyQeBjEfGfwIP0diG+TLtrHqrpUPgRsKscrd0I3AUcabimYY4Ae8vyXnr77AvtnyxH8/cAry5M16cpIgI4DJzIzC/1bWp73VdGxOay/Dbgw/QO3j0B3Fm6La574fXcCTyeZWd9WjLzvszcnpk76f3uPp6Zn6DFNY+k6YMawG3Az+ntP/510/Usqu1bwEvAG/RSfh+9fcCjwMlyf3npG/Q+SXkB+Cmwu6Ga/4TelPQZ4Hi53daBuv8YeLrU/SzwN6X9PcAPgTngn4FNpf2Ssj5Xtr+n4d+VDwEPd6nmi908o1FSpendB0ktYyhIqhgKkiqGgqSKoSCpYihIqhgKkiqGgqTK/wNDEJyPgB0hQQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 実行するたびに変わります\n",
    "\n",
    "# 画像データの読み込み\n",
    "index = 0\n",
    "imges, anno_class_imges = train_dataset.__getitem__(index)\n",
    "\n",
    "# 画像の表示\n",
    "img_val = imges\n",
    "img_val = img_val.numpy().transpose((1, 2, 0))\n",
    "plt.imshow(img_val)\n",
    "plt.show()\n",
    "\n",
    "# アノテーション画像の表示\n",
    "anno_file_path = train_anno_list[0]\n",
    "anno_class_img = Image.open(anno_file_path)   # [高さ][幅][色RGB]\n",
    "p_palette = anno_class_img.getpalette()\n",
    "\n",
    "anno_class_img_val = anno_class_imges.numpy()\n",
    "anno_class_img_val = Image.fromarray(np.uint8(anno_class_img_val), mode=\"P\")\n",
    "anno_class_img_val.putpalette(p_palette)\n",
    "plt.imshow(anno_class_img_val)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 検証画像の描画"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画像データの読み込み\n",
    "index = 0\n",
    "imges, anno_class_imges = val_dataset.__getitem__(index)\n",
    "\n",
    "# 画像の表示\n",
    "img_val = imges\n",
    "img_val = img_val.numpy().transpose((1, 2, 0))\n",
    "plt.imshow(img_val)\n",
    "plt.show()\n",
    "\n",
    "# アノテーション画像の表示\n",
    "anno_file_path = train_anno_list[0]\n",
    "anno_class_img = Image.open(anno_file_path)   # [高さ][幅][色RGB]\n",
    "p_palette = anno_class_img.getpalette()\n",
    "\n",
    "anno_class_img_val = anno_class_imges.numpy()\n",
    "anno_class_img_val = Image.fromarray(np.uint8(anno_class_img_val), mode=\"P\")\n",
    "anno_class_img_val.putpalette(p_palette)\n",
    "plt.imshow(anno_class_img_val)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以上"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
